Methods of Obtaining and Processing Information Reflecting the Electromagnetic Activity of the Brain

Authors

  • Vladyslav Holoborodko
  • Andrii Mazniev
  • Volodymyr Lyfar
  • Olena Lyfar

DOI:

https://doi.org/10.47839/ijc.25.2.4665

Keywords:

artificial intelligence, neural networks, diagnostics, data analysis, knowledge bases, clustering, Barycenter, electroencephalogram

Abstract

This paper presents methods for processing information obtained using an electroencephalograph to develop an effective human–machine interface based on mental control. The research focuses on clustering statistical data that reflects the brain’s electrical activity and determining its correlation with a person’s psycho-emotional state. A novel aspect of this approach is the classification of emotions into two primary types – acceptance (e.g., agreement, joy) and denial (e.g., rejection, grief) – with further gradation from slight to intense expression. The proposed method includes signal normalization, feature extraction, and the calculation of a barycenter for clustering EEG data. These techniques allow identifying emotional signatures corresponding to human perception at the time of EEG recording. The presented framework is designed for integration with artificial intelligence systems through machine learning algorithms. Its practical applications include medical diagnostics, cognitive training for professionals, and intelligent control systems. The approach enables real-time interpretation of emotional states, laying the groundwork for next-generation neural interfaces and adaptive control technologies.

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Published

2026-06-30

How to Cite

Holoborodko, V., Mazniev, A., Lyfar, V., & Lyfar, O. (2026). Methods of Obtaining and Processing Information Reflecting the Electromagnetic Activity of the Brain. International Journal of Computing, 25(2), 394-401. https://doi.org/10.47839/ijc.25.2.4665

Issue

2026, Volume 25, Issue 2

Section

Articles

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